StarPep Toolbox: an open-source software to assist chemical space analysis of bioactive peptides and their functions using complex networks.

MOTIVATION: Antimicrobial peptides (AMPs) are promising molecules to treat infectious diseases caused by multi-drug resistance pathogens, some types of cancer, and other conditions. Computer-aided strategies are efficient tools for the high-throughput screening of AMPs. RESULTS: This report highlights StarPep Toolbox, an open-source and user-friendly software to study the bioactive chemical space of AMPs using complex network-based representations, clustering, and similarity-searching models. The novelty of this research lies in the combination of network science and similarity-searching techniques, distinguishing it from conventional methods based on machine learning and other computational approaches. The network-based representation of the AMP chemical space presents promising opportunities for peptide drug repurposing, development, and optimization. This approach could serve as a baseline for the discovery of a new generation of therapeutics peptides. AVAILABILITY AND IMPLEMENTATION: All underlying code and installation files are accessible through GitHub (https://github.com/Grupo-Medicina-Molecular-y-Traslacional/StarPep) under the Apache 2.0 license.

Distributed and multicore QuBiLS-MIDAS software v2.0: Computing chiral, fuzzy, weighted and truncated geometrical molecular descriptors based on tensor algebra.

Advances to the distributed, multi-core and fully cross-platform QuBiLS-MIDAS software v2.0 (http://tomocomd.com/qubils-midas) are reported in this article since the v1.0 release. The QuBiLS-MIDAS software is the only one that computes atom-pair and alignment-free geometrical MDs (3D-MDs) from several distance metrics other than the Euclidean distance, as well as alignment-free 3D-MDs that codify structural information regarding the relations among three and four atoms of a molecule. The most recent features added to the QuBiLS-MIDAS software v2.0 are related (a) to the calculation of atomic weightings from indices based on the vertex-degree invariant (e.g., Alikhanidi index); (b) to consider central chirality during the molecular encoding; (c) to use measures based on clustering methods and statistical functions to codify structural information among more than two atoms; (d) to the use of a novel method based on fuzzy membership functions to spherically truncate inter-atomic relations; and (e) to the use of weighted and fuzzy aggregation operators to compute global 3D-MDs according to the importance and/or interrelation of the atoms of a molecule during the molecular encoding. Moreover, a novel module to compute QuBiLS-MIDAS 3D-MDs from their headings was also developed. This module can be used either by the graphical user interface or by means of the software library. By using the library, both the predictive models built with the QuBiLS-MIDAS 3D-MDs and the QuBiLS-MIDAS 3D-MDs calculation can be embedded in other tools. A set of predefined QuBiLS-MIDAS 3D-MDs with high information content and low redundancy on a set comprised of 20,469 compounds is also provided to be employed in further cheminformatics tasks. This set of predefined 3D-MDs evidenced better performance than all the universe of Dragon (v5.5) and PaDEL 0D-to-3D MDs in variability studies, whereas a linear independence study proved that these QuBiLS-MIDAS 3D-MDs codify chemical information orthogonal to the Dragon 0D-to-3D MDs. This set of predefined 3D-MDs would be periodically updated as long as new results be achieved. In general, this report highlights our continued efforts to provide a better tool for a most suitable characterization of compounds, and in this way, to contribute to obtaining better outcomes in future applications.

Graph-based data integration from bioactive peptide databases of pharmaceutical interest: toward an organized collection enabling visual network analysis.

MOTIVATION: Bioactive peptides have gained great attention in the academy and pharmaceutical industry since they play an important role in human health. However, the increasing number of bioactive peptide databases is causing the problem of data redundancy and duplicated efforts. Even worse is the fact that the available data is non-standardized and often dirty with data entry errors. Therefore, there is a need for a unified view that enables a more comprehensive analysis of the information on this topic residing at different sites. RESULTS: After collecting web pages from a large variety of bioactive peptide databases, we organized the web content into an integrated graph database (starPepDB) that holds a total of 71 310 nodes and 348 505 relationships. In this graph structure, there are 45 120 nodes representing peptides, and the rest of the nodes are connected to peptides for describing metadata. Additionally, to facilitate a better understanding of the integrated data, a software tool (starPep toolbox) has been developed for supporting visual network analysis in a user-friendly way; providing several functionalities such as peptide retrieval and filtering, network construction and visualization, interactive exploration and exporting data options. AVAILABILITY AND IMPLEMENTATION: Both starPepDB and starPep toolbox are freely available at http://mobiosd-hub.com/starpep/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Heterologous Machine Learning for the Identification of Antimicrobial Activity in Human-Targeted Drugs.

The emergence of microbes resistant to common antibiotics represent a current treat to human health. It has been recently recognized that non-antibiotic labeled drugs may promote antibiotic-resistance mechanisms in the human microbiome by presenting a secondary antibiotic activity; hence, the development of computer-assisted procedures to identify antibiotic activity in human-targeted compounds may assist in preventing the emergence of resistant microbes. In this regard, it is worth noting that while most antibiotics used to treat human infectious diseases are non-peptidic compounds, most known antimicrobials nowadays are peptides, therefore all computer-based models aimed to predict antimicrobials either use small datasets of non-peptidic compounds rendering predictions with poor reliability or they predict antimicrobial peptides that are not currently used in humans. Here we report a machine-learning-based approach trained to identify gut antimicrobial compounds; a unique aspect of our model is the use of heterologous training sets, in which peptide and non-peptide antimicrobial compounds were used to increase the size of the training data set. Our results show that combining peptide and non-peptide antimicrobial compounds rendered the best classification of gut antimicrobial compounds. Furthermore, this classification model was tested on the latest human-approved drugs expecting to identify antibiotics with broad-spectrum activity and our results show that the model rendered predictions consistent with current knowledge about broad-spectrum antibiotics. Therefore, heterologous machine learning rendered an efficient computational approach to classify antimicrobial compounds.

Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach.

BACKGROUND: Antimicrobial peptides are a promising alternative for combating pathogens resistant to conventional antibiotics. Computer-assisted peptide discovery strategies are necessary to automatically assess a significant amount of data by generating models that efficiently classify what an antimicrobial peptide is, before its evaluation in the wet lab. Model's performance depends on the selection of molecular descriptors for which an efficient and effective approach has recently been proposed. Unfortunately, how to adapt this method to the selection of molecular descriptors for the classification of antimicrobial peptides and the performance it can achieve, have only preliminary been explored. RESULTS: We propose an adaptation of this successful feature selection approach for the weighting of molecular descriptors and assess its performance. The evaluation is conducted on six high-quality benchmark datasets that have previously been used for the empirical evaluation of state-of-art antimicrobial prediction tools in an unbiased manner. The results indicate that our approach substantially reduces the number of required molecular descriptors, improving, at the same time, the performance of classification with respect to using all molecular descriptors. Our models also outperform state-of-art prediction tools for the classification of antimicrobial and antibacterial peptides. CONCLUSIONS: The proposed methodology is an efficient approach for the development of models to classify antimicrobial peptides. Particularly in the generation of models for discrimination against a specific antimicrobial activity, such as antibacterial. One of our future directions is aimed at using the obtained classifier to search for antimicrobial peptides in various transcriptomes.

Overlap and diversity in antimicrobial peptide databases: compiling a non-redundant set of sequences.

MOTIVATION: The large variety of antimicrobial peptide (AMP) databases developed to date are characterized by a substantial overlap of data and similarity of sequences. Our goals are to analyze the levels of redundancy for all available AMP databases and use this information to build a new non-redundant sequence database. For this purpose, a new software tool is introduced. RESULTS: A comparative study of 25 AMP databases reveals the overlap and diversity among them and the internal diversity within each database. The overlap analysis shows that only one database (Peptaibol) contains exclusive data, not present in any other, whereas all sequences in the LAMP_Patent database are included in CAMP_Patent. However, the majority of databases have their own set of unique sequences, as well as some overlap with other databases. The complete set of non-duplicate sequences comprises 16 990 cases, which is almost half of the total number of reported peptides. On the other hand, the diversity analysis identifies the most and least diverse databases and proves that all databases exhibit some level of redundancy. Finally, we present a new parallel-free software, named Dover Analyzer, developed to compute the overlap and diversity between any number of databases and compile a set of non-redundant sequences. These results are useful for selecting or building a suitable representative set of AMPs, according to specific needs.

A Novel Network Science and Similarity-Searching-Based Approach for Discovering Potential Tumor-Homing Peptides from Antimicrobials.

Peptide-based drugs are promising anticancer candidates due to their biocompatibility and low toxicity. In particular, tumor-homing peptides (THPs) have the ability to bind specifically to cancer cell receptors and tumor vasculature. Despite their potential to develop antitumor drugs, there are few available prediction tools to assist the discovery of new THPs. Two webservers based on machine learning models are currently active, the TumorHPD and the THPep, and more recently the SCMTHP. Herein, a novel method based on network science and similarity searching implemented in the starPep toolbox is presented for THP discovery. The approach leverages from exploring the structural space of THPs with Chemical Space Networks (CSNs) and from applying centrality measures to identify the most relevant and non-redundant THP sequences within the CSN. Such THPs were considered as queries (Qs) for multi-query similarity searches that apply a group fusion (MAX-SIM rule) model. The resulting multi-query similarity searching models (SSMs) were validated with three benchmarking datasets of THPs/non-THPs. The predictions achieved accuracies that ranged from 92.64 to 99.18% and Matthews Correlation Coefficients between 0.894-0.98, outperforming state-of-the-art predictors. The best model was applied to repurpose AMPs from the starPep database as THPs, which were subsequently optimized for the TH activity. Finally, 54 promising THP leads were discovered, and their sequences were analyzed to encounter novel motifs. These results demonstrate the potential of CSNs and multi-query similarity searching for the rapid and accurate identification of THPs.

Network Science and Group Fusion Similarity-Based Searching to Explore the Chemical Space of Antiparasitic Peptides.

Antimicrobial peptides (AMPs) have appeared as promising compounds to treat a wide range of diseases. Their clinical potentialities reside in the wide range of mechanisms they can use for both killing microbes and modulating immune responses. However, the hugeness of the AMPs' chemical space (AMPCS), represented by more than 1065 unique sequences, has represented a big challenge for the discovery of new promising therapeutic peptides and for the identification of common structural motifs. Here, we introduce network science and a similarity searching approach to discover new promising AMPs, specifically antiparasitic peptides (APPs). We exploited the network-based representation of APPs' chemical space (APPCS) to retrieve valuable information by using three network types: chemical space (CSN), half-space proximal (HSPN), and metadata (METN). Some centrality measures were applied to identify in each network the most important and nonredundant peptides. Then, these central peptides were considered as queries (Qs) in group fusion similarity-based searches against a comprehensive collection of known AMPs, stored in the graph database StarPepDB, to propose new potential APPs. The performance of the resulting multiquery similarity-based search models (mQSSMs) was evaluated in five benchmarking data sets of APP/non-APPs. The predictions performed by the best mQSSM showed a strong-to-very-strong performance since their external Matthews correlation coefficient (MCC) values ranged from 0.834 to 0.965. Outstanding MCC values (>0.85) were attained by the mQSSM with 219 Qs from both networks CSN and HSPN with 0.5 as similarity threshold in external data sets. Then, the performance of our best mQSSM was compared with the APPs prediction servers AMPDiscover and AMPFun. The proposed model showed its relevance by outperforming state-of-the-art machine learning models to predict APPs. After applying the best mQSSM and additional filters on the non-APP space from StarPepDB, 95 AMPs were repurposed as potential APP hits. Due to the high sequence diversity of these peptides, different computational approaches were applied to identify relevant motifs for searching and designing new APPs. Lastly, we identified 11 promising APP lead candidates by using our best mQSSMs together with diversity-based network analyses, and 24 web servers for activity/toxicity and drug-like properties. These results support that network-based similarity searches can be an effective and reliable strategy to identify APPs. The proposed models and pipeline are freely available through the StarPep toolbox software at http://mobiosd-hub.com/starpep.

Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach.

The increasing interest in bioactive peptides with therapeutic potentials has been reflected in a large variety of biological databases published over the last years. However, the knowledge discovery process from these heterogeneous data sources is a nontrivial task, becoming the essence of our research endeavor. Therefore, we devise a unified data model based on molecular similarity networks for representing a chemical reference space of bioactive peptides, having an implicit knowledge that is currently not explicitly accessed in existing biological databases. Indeed, our main contribution is a novel workflow for the automatic construction of such similarity networks, enabling visual graph mining techniques to uncover new insights from the "ocean" of known bioactive peptides. The workflow presented here relies on the following sequential steps: (i) calculation of molecular descriptors by applying statistical and aggregation operators on amino acid property vectors; (ii) a two-stage unsupervised feature selection method to identify an optimized subset of descriptors using the concepts of entropy and mutual information; (iii) generation of sparse networks where nodes represent bioactive peptides, and edges between two nodes denote their pairwise similarity/distance relationships in the defined descriptor space; and (iv) exploratory analysis using visual inspection in combination with clustering and network science techniques. For practical purposes, the proposed workflow has been implemented in our visual analytics software tool ( http://mobiosd-hub.com/starpep/ ), to assist researchers in extracting useful information from an integrated collection of 45120 bioactive peptides, which is one of the largest and most diverse data in its field. Finally, we illustrate the applicability of the proposed workflow for discovering central nodes in molecular similarity networks that may represent a biologically relevant chemical space known to date.

Multi-Server Approach for High-Throughput Molecular Descriptors Calculation based on Multi-Linear Algebraic Maps.

The present report introduces a novel module of the QuBiLS-MIDAS software for the distributed computation of the 3D Multi-Linear algebraic molecular indices. The main motivation for developing this module is to deal with the computational complexity experienced during the calculation of the descriptors over large datasets. To accomplish this task, a multi-server computing platform named T-arenal was developed, which is suited for institutions with many workstations interconnected through a local network and without resources particularly destined for computation tasks. This new system was deployed in 337 workstations and it was perfectly integrated with the QuBiLS-MIDAS software. To illustrate the usability of the T-arenal platform, performance tests over a dataset comprised of 15 000 compounds are carried out, yielding a 52 and 60 fold reduction in the sequential processing time for the 2-Linear and 3-Linear indices, respectively. Therefore, it can be stated that the T-arenal based distribution of computation tasks constitutes a suitable strategy for performing high-throughput calculations of 3D Multi-Linear descriptors over thousands of chemical structures for posterior QSAR and/or ADME-Tox studies.